Sample-efficient adaptive text-to-speech

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers. The adaptive audio-generation model is adapted for a new individual speaker using adaptation data comprising second text and audio data representing the new individual speaker speaking portions of the second text, the new individual speaker being different from each of the plurality of individual speakers, wherein adapting the audio-generation model includes learning a new embedding vector for the new individual speaker.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119(a) of thefiling date of Greek Patent Application No. 20180100486, filed in theGreek Patent Office on Oct. 26, 2018. The disclosure of the foregoingapplication is herein incorporated by reference in its entirety.

BACKGROUND

This specification relates to signal-generation neural networks.

Neural networks are machine learning models that employ one or morelayers of nonlinear units to predict an output for a received input.Some neural networks include one or more hidden layers in addition to anoutput layer. The output of each hidden layer is used as input to thenext layer in the network, i.e., the next hidden layer or the outputlayer. Each layer of the network generates an output from a receivedinput in accordance with current values of a respective set ofparameters.

One example of a neural network is an audio generation neural network.Audio generation neural networks take text as input and generate asoutput a raw audio waveform of a speaker speaking the text. Realisticaudio generation typically requires multiple thousands of samples to begenerated per second, e.g., 24,000 samples per second. One example of anaudio generation neural network is a WaveNet. WaveNets were initiallydescribed in van den Oord et al., WaveNet: A Generative Model for RawAudio, in arXiv preprint arXiv:1609.03499 (2016), available atarxiv.org. A WaveNet is a deep neural network that models theconditional probability of an audio sample having a particular valuegiven a particular number of previously occurring sample values.

One of the fundamental limitations of realistic audio generation neuralnetworks is that they require large training datasets. Typically, atraining system needs hours of audio recordings for each individualspeaker represented in the training data. This amount of data isexpensive and cumbersome to obtain and curate in general, and there aremany use cases in which this volume of training data is impractical orimpossible to obtain. For example, one application of text-to-speechmodels is providing realistic speech capabilities for medical patientswho suffer voice-impairing medical conditions. Such patients typicallydo not have access to hours of audio recordings of themselves speaking.

SUMMARY

This specification describes a system implemented as computer programson one or more computers in one or more locations that can generate asample-efficient, adaptive audio-generation model. In this context,being sample-efficient and adaptive means that the model can becustomized to emulate the speech of a new speaker with far less trainingdata than was used to train the adaptive model. For example, whiletraining the adaptive model may require hours of audio recordings foreach individual speaker, adapting the model for a new speaker mayrequire only a few minutes of audio recordings of the new speaker.

A training system can train the audio-generation model using a pluralityof embedding vectors for respective individual speakers and anaudio-generation neural network. Because of the computationallyintensive nature of the training process, the training can be performedby a distributed computing system, e.g., a datacenter, having hundredsor thousands of computers.

The output of the training process is an adaptive audio-generation modelthat can be efficiently adapted to a new speaker. Adapting the modelgenerally involves learning a new embedding vector for the new speaker,and may optionally involve fine-tuning the parameters of the neuralnetwork for the new speaker. The adaptation data can be only a fewseconds or a few minutes of audio recordings of the new speaker. Theadaptation process is therefore much less computationally intensive thanthe original training process. Thus, the adaptation process can beperformed on much less powerful hardware, e.g., a mobile phone oranother wearable device, a desktop or laptop computer, or anotherinternet-enabled device installed in a user's home, to name just a fewexamples.

The subject matter described in this specification can be implemented inparticular embodiments so as to realize one or more of the followingadvantages. Adaptive audio-generation model can be used to rapidly adaptto a new speaker using orders of magnitude less data than was used totrain the model. This enables the adaptation process to be performed byconsumer hardware of end users rather than being performed in adatacenter.

The adaptation technology enables a variety of technological use casesthat were previously not possible. For example, realistic voicetranslations of video or audio content can be generated that emulate thecharacteristics of a particular speaker without requiring hours of audiorecordings of the speaker. As another example, real-time translatedvideo conferencing or phone calls can be generated in which thetranslation realistically emulates the speaker who does not actuallyspeak the translated language. In addition, an adaptive audio generationmodel can be used to provide realistic speech capabilities for medicalpatients suffering voice-impairing medical conditions without requiringsuch patients to have hours of audio recordings of themselves speaking.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram that illustrates an example architecture fortraining a sample-efficient, adaptive audio-generation model.

FIG. 1B is a diagram that illustrates an example architecture foradapting a sample-efficient, adaptive audio-generation model to a newindividual speaker.

FIG. 1C is a diagram that illustrates an example architecture 100 c forperforming inference using an adapted sample-efficient, adaptiveaudio-generation model.

FIG. 2 is a flowchart of an example process for generating and using asample-efficient, adaptive audio-generation model.

FIG. 3 is a diagram that illustrates an example architecture 300 for anembedding encoder for predicting speaker embeddings.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIGS. 1A, 1B, and 1C respectively illustrate example architectures fortraining, adapting, and performing inference using a sample-efficient,adaptive audio-generation model. The examples in this specificationgenerally discuss using a WaveNet architecture to implement theaudio-generation model. However, the same techniques can also be appliedusing any other appropriate neural audio-generation model, e.g., theWaveRNN model described in Kalchbrenner et al., Efficient Neural AudioSynthesis, in arXiv preprint arXiv:1802.08435 (2016), available atarxiv.org, and the Tacotron 2 model described in Shen et al., NaturalTTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions, inarXiv preprint arXiv:1712.05884 (2017), available at arxiv.org, to namejust a few examples.

FIG. 1A is a diagram that illustrates an example architecture 100 a fortraining a sample-efficient, adaptive audio-generation model. Thearchitecture 100 a includes a WaveNet Stack 110 a that is trained usingan embedding table 120 that stores embedding vectors for multipledifferent respective individual speakers, each individual speaker havinga speaker index 105. The components illustrated in FIG. 1A can beimplemented by a distributed computing system comprising a plurality ofcomputers that coordinate to train the WaveNet Stack 110 a. As describedabove, the training process can use many different individual speakersusing hours of audio waveform data.

During each iteration of training, text 115 and a waveform 125 of audiospoken by an individual corresponding to a value of the speaker index isused as input to the WaveNet Stack 110 a. The computing systemperforming the training process can then iterate over each individual inthe embedding table 120 and optionally over multiple different segmentsof text for each of the individuals.

The WaveNet Stack 110 a can be implemented as is an autoregressive modelthat factorizes the joint probability distribution of a waveform, x={x1,. . . , xT}, into a product of conditional distributions using theprobabilistic chain rule:

${{p\left( {{xh};w} \right)} = {\prod\limits_{t = 1}^{T}\; {p\left( {{x_{t}x_{{1\text{:}t} - 1}},{h;w}} \right)}}},$

where x_(t) is the t-th timestep sample, and h and w are respectivelythe conditioning inputs and parameters of the model.

To train a multi-speaker WaveNet, the conditioning inputs h consist ofthe speaker identity s, the linguistic features l, and the logarithmicfundamental frequency f0 values. The variable l encodes the sequence ofphonemes derived from the input text, and f0 controls the dynamics ofthe pitch in the generated utterance. Given the speaker index s for eachutterance in the dataset, the model can be expressed as:

${{p\left( {{xl},{f_{0};e_{s}},w} \right)} = {\prod\limits_{t = 1}^{T}\; {p\left( {{x_{t}x_{{1\text{:}t} - 1}},l,{f_{0};e_{s}},w} \right)}}},$

where the embedding table 120 of speaker embedding vectors e_(s) islearned alongside the standard WaveNet parameters. These vectors capturesalient voice characteristics across individual speakers and provide aconvenient mechanism for generalizing WaveNet to the sample-efficientadaptation techniques described in this specification.

The linguistic features l 145 and the fundamental frequency values f₀135 can each be a respective time-series with a lower sampling frequencythan the waveform 125. Thus, to be used as local conditioning variables,the linguistic features 145 and fundamental frequency values 135 can beupsampled by a transposed convolutional network. During training, l 145and f₀ 135 can be extracted by signal processing methods from pairs oftraining utterance and transcript. During testing, those values can bepredicted from text by existing models.

FIG. 1B is a diagram that illustrates an example architecture 100 b foradapting a sample-efficient, adaptive audio-generation model to a newindividual speaker. During the adaptation process, the parameterslearned during training are adjusted so that they are adapted to aparticular individual's voice characteristics. In other words, thepurpose of the training process illustrated in FIG. 1A is to learn aprior. During adaptation, this prior is combined with new data torapidly adapt to a new speakers' voice characteristics.

The architecture 100 b includes a WaveNet Stack 110 b whose parametershave been adjusted according to a new embedding vector 122 thatrepresents the characteristics of a new speaker. The new embeddingvector 122 can be generated in a variety of ways, which are discussed inmore detail below with reference to FIG. 2.

FIG. 1C is a diagram that illustrates an example architecture 100 c forperforming inference using an adapted sample-efficient, adaptiveaudio-generation model. During the inference process, new text 155 andthe embedding vector 122 generated for the new speaker are used as inputto generate an output waveform 165 having characteristics of the newspeaker. As part of this process, a predictor 150 that uses existingmodels to generate linguistic features and fundamental frequencies ofthe new speaker 150 from the new text 155.

FIG. 2 is a flowchart of an example process for generating and using asample-efficient, adaptive audio-generation model. As described above,the process includes three stages: training, adaptation, and inference.Typically the training stage is performed on a distributed computingsystem having multiple computers. And as described above, the other twostages can be performed on much less computationally expensive hardware,e.g., a desktop computer, laptop computer, or mobile computing device.For convenience, the example process will be described as beingperformed by a system of one or more computers.

The system generates an adaptive audio-generation model using datarepresenting audio spoken by a plurality of different individualspeakers (210). As described above with reference to FIG. 1A, the systemcan generate different embedding vectors for a plurality of individualspeakers. The system can then train parameter values of a neuralaudio-generation model, e.g., a WaveNet stack, a WaveRNN model, or aTacotron 2 model, using training data that includes text and audio datarepresenting a plurality of different individual speakers speakingportions of the text. Each of the embedding vectors generally representsrespective voice characteristics of the plurality of differentindividual speakers.

The system adapts the adaptive audio-generation model for a newindividual speaker using data representing audio spoken by the newindividual speaker (220). As described above with reference to FIG. 1B,the adaptation process uses audio data representing the new individualspeaker speaking portions of text.

Generally the data used for the adaptation process can be orders ofmagnitude smaller than data used for the training process. In someimplementations, the training data comprises multiple hours of audiorecordings for each individual speaker of the plurality of differentindividual speakers, while the adaptation process can use less than tenminutes of audio recordings of the new individual speaker.

In addition, the adaptation process is generally much lesscomputationally intensive than the training process. Thus, in someimplementations the training process is performed in a datacenter havingtens or hundreds or thousands of computers, while the adaptation processis performed on a mobile device or a single, Internet-enabled device.

The system can generate a new embedding vector using the audio data tobe used for adapting the model to the new individual speaker. Generally,the new embedding vector can be different from any of the embeddingvectors used during the training process.

The adaptation process can be performed in multiple ways. In particular,the system can use a non-parametric technique or a parametric technique.

The non-parametric technique involves adapting the new speakerembeddings, the model parameters, or both, using held-asidedemonstration data. For example, the system can fine-tune the modelparameters by retraining with respect to held-aside adaptation data.

For example, when training a WaveNet model to maximize the conditionallog-likelihood of the generated audio, the system can jointly optimizeboth the set of speaker parameters {e_(s)} and the shared WaveNet coreparameters w. The system can then extend the model to a new speaker byextracting the l and f₀ features from their adaptation data and randomlyinitializing a new embedding vector e. The new embedding vector e canthen be optimized such that the demonstration waveforms, {x(1)_(demo), .. . x(n)_(demo)} paired with features {l⁽¹⁾ _(demo), f₀ ⁽¹⁾ _(demo)), .. . (l^((n)) _(demo), f^((n)) _(demo))} that are most likely, e.g., bysatisfying a likelihood threshold under the model with w fixed(SEA-EMB):

$e_{demo} = {\underset{e}{\arg \mspace{11mu} \max}{\sum\limits_{i}\; {\log \; {{p\left( {{x_{demo}^{(i)}l_{demo}^{(i)}},{f_{0,{demo}}^{(i)};e},w} \right)}.}}}}$

Alternatively, all of the model parameters may be additionallyfine-tuned (SEA-ALL):

$\left( {e_{demo},w_{finetuned}} \right) = {\underset{e,w}{\arg \mspace{11mu} \max}{\sum\limits_{i}\; {\log \; {{p\left( {{x_{demo}^{(i)}l_{demo}^{(i)}},{f_{0,{demo}}^{(i)};e},w} \right)}.}}}}$

Both non-parametric approaches to sample-efficient voice adaptation asthe number of embedding vectors scales with the number of speakers.However, the training processes are slightly different. Because theSEA-EMB method optimizes only a low-dimensional vector, it is far lessprone to overfitting, and the system is therefore able to retrain themodel to convergence even with mere seconds of adaptation data. Bycontrast, the SEA-ALL has many more parameters that might overfit to theadaptation data. To address this issue, the system can hold out aparticular fraction, e.g., 5%, 10%, or 15%, of the demonstration datafor calculating a standard early termination criterion. The system canalso pre-initialize e with the optimal value from the SEA-EMB method,which can significantly improve the generalization performance even withjust a few seconds of adaptation data.

Alternatively or in addition, the system can use a parametric techniquethat involves training an auxiliary network to predict the embeddingvector of a new speaker using the demonstration data.

In contrast to the non-parametric approach, whereby a differentembedding vector is fitted for each speaker, the system can train anauxiliary encoder network to predict an embedding vector for a newspeaker given their demonstration data. Specifically, the system canmodel:

${{p\left( {{xl},f_{0},x_{demo},l_{demo},{f_{0,{demo}};w}} \right)} = {\prod\limits_{t = 1}^{T}\; {p\left( {{x_{t}x_{{1\text{:}t} - 1}},l,{f_{0};{e\left( {x_{demo},l_{demo},f_{0,{demo}}} \right)}},w} \right)}}},$

where for each training example, a randomly selected demonstrationutterance from that speaker is included in addition to the regularconditioning inputs. The full WaveNet model and the encoder network e( )can then be trained together from scratch. This technique is describedin more detail below with reference to FIG. 3.

In general, the parametric approach (SEA-ENC) exhibits the advantage ofbeing trained in a transcript-independent setting given only the inputwaveform, e(x_(demo)), and requires negligible computation at adaptationtime. However, the learned encoder can also introduce bias when fittingan embedding due to its limited network capacity.

The system performs a text-to-speech inference process to convert textinto an output waveform having characteristics of the new individualspeaker (230). In general, the system uses the audio-generation modeladapted for the new individual speaker, which includes using as inputthe new embedding vector for the individual speaker and features of anew portion of text. This process can also include automaticallygenerating audio of a translation of video or audio content in which theaudio of the translation is adapted to have the characteristics of thenew speaker. As one example, this process can be performed during avideo conference or a phone call such that audio of the translation isadapted to the characteristics of the new speaker.

FIG. 3 is a diagram that illustrates an example architecture 300 for anembedding encoder for predicting speaker embeddings. In general, theembedding encoder takes as input a demo waveform 310 and generates apredicted speaker embedding 320. In other words, the embedding encoderpredicts an embedding vector for a speaker when given adaptation datafor the speaker. The embedding encoder network is illustrated as thesummation of the output of two sub-networks 330 and 340.

The first subnetwork 330 is a pre-trained speaker verification model(TI-SV) comprising 3 LSTM layers and a single linear layer. The firstsubnetwork 330 generally maps a waveform sequence of arbitrary length toa fixed dimensional d-vector with a sliding window. The first subnetwork330 can be trained from utterances of speakers extracted from anonymizedvoice search logs. The first subnetwork 330 also includes a shallow MLPto project the output d-vector to the speaker embedding space.

The second subnetwork 340 includes 16 1-D convolutional layers. Thesecond subnetwork 340 essentially reduces the temporal resolution, e.g.,to 256 ms per frame for 16 kHz audio), and then averages across time andprojects into the speaker embedding space. The purpose of the secondsubnetwork 340 is to extract residual speaker information present in thedemonstration waveforms but not captured by the pre-trained TI-SV model.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code, can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages; and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

This specification uses the term “configured” in connection with systemsand computer program components. For a system of one or more computersto be configured to perform particular operations or actions means thatthe system has installed on it software, firmware, hardware, or acombination of them that in operation cause the system to perform theoperations or actions. For one or more computer programs to beconfigured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions.

In this specification, the term “database” is used broadly to refer toany collection of data: the data does not need to be structured in anyparticular way, or structured at all, and it can be stored on storagedevices in one or more locations. Thus, for example, the index databasecan include multiple collections of data, each of which may be organizedand accessed differently.

Similarly, in this specification the term “engine” is used broadly torefer to a software-based system, subsystem, or process that isprogrammed to perform one or more specific functions. Generally, anengine will be implemented as one or more software modules orcomponents, installed on one or more computers in one or more locations.In some cases, one or more computers will be dedicated to a particularengine; in other cases, multiple engines can be installed and running onthe same computer or computers.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.Also, a computer can interact with a user by sending text messages orother forms of message to a personal device, e.g., a smartphone that isrunning a messaging application, and receiving responsive messages fromthe user in return.

Data processing apparatus for implementing machine learning models canalso include, for example, special-purpose hardware accelerator unitsfor processing common and compute-intensive parts of machine learningtraining or production, i.e., inference, workloads.

Machine learning models can be implemented and deployed using a machinelearning framework, e.g., a TensorFlow framework, a Microsoft CognitiveToolkit framework, an Apache Singa framework, or an Apache MXNetframework.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In some cases, multitasking and parallel processing may beadvantageous.

What is claimed is:
 1. A method performed by one or more computers, themethod comprising: generating an adaptive audio-generation modelincluding learning a plurality of embedding vectors and parameter valuesof a neural network using training data comprising first text and audiodata representing a plurality of different individual speakers speakingportions of the first text, wherein the plurality of embedding vectorsrepresent respective voice characteristics of the plurality of differentindividual speakers; and adapting the adaptive audio-generation modelfor a new individual speaker using adaptation data comprising secondtext and audio data representing the new individual speaker speakingportions of the second text, the new individual speaker being differentfrom each of the plurality of individual speakers, wherein adapting theaudio-generation model includes learning a new embedding vector for thenew individual speaker.
 2. The method of claim 1, wherein theaudio-generation model adapted for the new individual speaker does notuse any of the plurality of embedding vectors for the plurality ofindividual speakers in the training data.
 3. The method of claim 1,wherein generating the adaptive audio-generation model compriseslearning a different respective embedding vector for each of theplurality of individual speakers.
 4. The method of claim 3, whereinadapting the adaptive audio-generation model comprises extractingfeatures from the adaptation data and learning values of the newembedding vector that are most likely according to the learned parametervalues of the audio-generation model.
 5. The method of claim 3, whereinadapting the adaptive audio-generation model comprises extractingfeatures from the adaptation data and learning both values of the newembedding vector and new parameter values for the neural network.
 6. Themethod of claim 5, further comprising: pre-initializing the newembedding vector with values learned from the features of the adaptationdata.
 7. The method of claim 1, wherein generating the adaptiveaudio-generation model comprises learning an encoder network that isconfigured to predict an embedding vector for a speaker when givenadaptation data for the speaker.
 8. The method of claim 7, wherein theencoder network comprises a summation of two subnetworks comprising 1) apre-trained speaker verification model, and 2) a second subnetworkcomprising a plurality of convolutional layers configured to learnresidual features of individual speakers.
 9. The method of claim 1,further comprising: performing text-to-speech generation using theaudio-generation model adapted for the new individual speaker, includingusing as input the learned new embedding vector and features of a newportion of text.
 10. The method of claim 9, wherein performingtext-to-speech generation comprises automatically generating audio of atranslation of video or audio content, the audio of the translationbeing adapted to the characteristics of the new speaker.
 11. The methodof claim 9, wherein performing text-to-speech generation comprisesautomatically generating audio of a translation of speech during a videoconference or a phone call, the audio of the translation being adaptedto the characteristics of the new speaker.
 12. The method of claim 1,wherein the training data comprises multiple hours of audio recordingsfor each individual speaker of the plurality of different individualspeakers, and wherein the adaptation data comprises less than tenminutes of audio recordings of the new individual speaker.
 13. Themethod of claim 1, wherein generating the adaptive audio-generationmodel comprises training the model in a datacenter, and wherein adaptingthe audio-generation model for the new individual speaker comprisesadapting the model on a mobile device or a single internet-enableddevice.
 14. A system comprising: one or more computers and one or morestorage devices storing instructions that are operable, when executed bythe one or more computers, to cause the one or more computers to performoperations comprising: generating an adaptive audio-generation modelincluding learning a plurality of embedding vectors and parameter valuesof a neural network using training data comprising first text and audiodata representing a plurality of different individual speakers speakingportions of the first text, wherein the plurality of embedding vectorsrepresent respective voice characteristics of the plurality of differentindividual speakers; and adapting the adaptive audio-generation modelfor a new individual speaker using adaptation data comprising secondtext and audio data representing the new individual speaker speakingportions of the second text, the new individual speaker being differentfrom each of the plurality of individual speakers, wherein adapting theaudio-generation model includes learning a new embedding vector for thenew individual speaker.
 15. The system of claim 14, wherein theaudio-generation model adapted for the new individual speaker does notuse any of the plurality of embedding vectors for the plurality ofindividual speakers in the training data.
 16. The system of claim 14,wherein generating the adaptive audio-generation model compriseslearning a different respective embedding vector for each of theplurality of individual speakers.
 17. The system of claim 16, whereinadapting the adaptive audio-generation model comprises extractingfeatures from the adaptation data and learning values of the newembedding vector that are most likely according to the learned parametervalues of the audio-generation model.
 18. The system of claim 16,wherein adapting the adaptive audio-generation model comprisesextracting features from the adaptation data and learning both values ofthe new embedding vector and new parameter values for the neuralnetwork.
 19. The system of claim 18, wherein the operations furthercomprise: pre-initializing the new embedding vector with values learnedfrom the features of the adaptation data.
 20. One or more non-transitorycomputer storage media encoded with computer program instructions thatwhen executed by one or more computers cause the one or more computersto perform operations comprising: generating an adaptiveaudio-generation model including learning a plurality of embeddingvectors and parameter values of a neural network using training datacomprising first text and audio data representing a plurality ofdifferent individual speakers speaking portions of the first text,wherein the plurality of embedding vectors represent respective voicecharacteristics of the plurality of different individual speakers; andadapting the adaptive audio-generation model for a new individualspeaker using adaptation data comprising second text and audio datarepresenting the new individual speaker speaking portions of the secondtext, the new individual speaker being different from each of theplurality of individual speakers, wherein adapting the audio-generationmodel includes learning a new embedding vector for the new individualspeaker.